Back to Search Start Over

Time of Your Hate: The Challenge of Time in Hate Speech Detection on Social Media

Authors :
Komal Florio
Valerio Basile
Marco Polignano
Pierpaolo Basile
Viviana Patti
Source :
Applied Sciences, Vol 10, Iss 12, p 4180 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

The availability of large annotated corpora from social media and the development of powerful classification approaches have contributed in an unprecedented way to tackle the challenge of monitoring users’ opinions and sentiments in online social platforms across time. Such linguistic data are strongly affected by events and topic discourse, and this aspect is crucial when detecting phenomena such as hate speech, especially from a diachronic perspective. We address this challenge by focusing on a real case study: the “Contro l’odio” platform for monitoring hate speech against immigrants in the Italian Twittersphere. We explored the temporal robustness of a BERT model for Italian (AlBERTo), the current benchmark on non-diachronic detection settings. We tested different training strategies to evaluate how the classification performance is affected by adding more data temporally distant from the test set and hence potentially different in terms of topic and language use. Our analysis points out the limits that a supervised classification model encounters on data that are heavily influenced by events. Our results show how AlBERTo is highly sensitive to the temporal distance of the fine-tuning set. However, with an adequate time window, the performance increases, while requiring less annotated data than a traditional classifier.

Details

Language :
English
ISSN :
20763417
Volume :
10
Issue :
12
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.0e390ae9a600456786a577693a5fab06
Document Type :
article
Full Text :
https://doi.org/10.3390/app10124180